I. Field
Example aspects described herein generally relate to media recommendation and, more particularly, to cross-media recommendation.
II. Related Art
Cross-media recommendation, also known as cross-domain recommendation, utilizes user preferences in one domain (e.g., music) to suggest recommendations in another domain (e.g., books). The problem of using data across separated domains is not trivial. One conventional way of providing cross-media recommendation is to combine two or more domain systems into one system and then generate a recommendation using traditional one-domain methods, for example, by basing the recommendation on user ratings and characteristics about the items themselves (e.g., title, song, type of product, sales, etc.).
Known techniques cannot easily deal with users who have made very few ratings. Moreover, the huge collections of heterogeneous media data might not contain the same descriptors, making it difficult for existing recommender systems to find patterns or correlations across domains.
These types of issues have made it technically challenging for media recommender providers to develop services and products that can make an accurate recommendations for a specific person that maximizes the diversity of media across multiple domains.